The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challenging for citizens to differentiate between accurate and inaccurate information about COVID-19. This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets. The study makes comparisons alongside four key aspects: 1) the distribution of topics, 2) the live status of tweets, 3) language analysis and 4) the spreading power over time. An added contribution of this study is the creation of a COVID-19 misinformation classification dataset. Finally, we demonstrate that this new dataset helps improve misinformation classification by more than 9\% based on average F1 measure.
@article{arxiv.2304.04811,
title = {A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation},
author = {Yida Mu and Ye Jiang and Freddy Heppell and Iknoor Singh and Carolina Scarton and Kalina Bontcheva and Xingyi Song},
journal= {arXiv preprint arXiv:2304.04811},
year = {2023}
}